Lectures

Lecture 22, March 31st, 2016: Approximate Inference

In this lecture we will continue our discussion of probabilistic modelling and turn our attention to approximate inference.

Please study the following material in preparation for the class:

  • Chapter 19 of the Deep Learning Textbook on approximate inference.

In preparation for the following lecture, please study this paper as well, mentioned already in class:

 

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Ordering test set Dogs vs. Cats

The test set of the Dogs vs. Cats in Fuel was ordered based on strings instead of numerically (i.e. 1, 10, 11, …, 2, … instead of 1, 2, …). When submitting to Kaggle it expects the latter, which meant that the test scores did not make sense. This was fixed in Fuel pull request #336. If you are using the cluster, the dataset has been updated (if you need the old HDF5 file it can be found under dogs_vs_cats.old.hdf5). If you run your own installation, please update Fuel and rerun fuel-convert dogs_vs_cats.

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Lectures

Lecture 21, March 24th, 2016: RBMs and Partition Function

In this lecture we will continue our discussion of probabilistic undirected graphical models such as the Restricted Boltzmann Machine.

Please study the following material in preparation for the class:

 

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Announcements

Project leaderboard and deadline

Some students have asked about the deadline for the class project. The deadline will be 4 weeks from now (the Monday after classes end) on 18 April.

As was mentioned in class, we put up a leaderboard where we ask you to submit the results you achieved on either project (classification scores for Dogs vs. Cats and samples or perplexity scores for the vocal synthesis task). Don’t wait until the end to do so, please put up intermediate results as well!

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Announcements

Final Exam

The final exam is in-class and scheduled for the last day of class, Thursday April 14, 2016, at the usual 9:30-11:30 class time.

You are not allowed to open up your phone or laptop (or any other means to connect to the internet) during the exam, but you are allowed to bring your own 8.5×11 cheat sheet (2-pager).

As a guide (but thing might be different), here are some exams from previous years.

 

 

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Lectures

Lecture 20, March 21st, 2016: Graphical Models

In this lecture we will begin our discussion of probabilistic undirected graphical models.

Please study the following material in preparation for the class:

  • Lecture 5 (5.1 to 5.3) of Hugo Larochelle’s course on Neural Networks.
  • Chapter 16 of the Deep Learning Textbook (important background on probabilistic models).
  • Chapter 17 of the Deep Learning Textbook (Monte-Carlo methods)

Other relevant material:

  •  Lecture 11  of Geoff Hinton’s cousera course on Neural Networks (from Hopfield nets to Boltzmann machines)
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